Box and Violin Plots show the distribution of each feature value across the instances. Box plots show the quantiles of the distribution and violin plots show the approximated probability density of the feature values. Such plots are useful to inspect the instances and to detect characteristics of the instances. For example, if the distributions have two or more modes, it could indicate that the instance set is heterogeneous which could cause problems in combination with racing strategies configurators typically use. NaN values are removed from the data.
runscontainer: RunsContainer contains all important information about the configurator runs
box_violin(output_dir, scenario, feat_names, feat_importance)¶
get_html(d=None, tooltip=None) → Tuple[str, str]¶
General reports in html-format, to be easily integrated in html-code. ALSO FOR BOKEH-OUTPUT.
d (Dictionary) – a dictionary that will be later turned into a website
script, div – header and body part of html-code
- Return type
Depending on analysis, this creates jupyter-notebook compatible output.
This function needs to be called if bokeh-plots are to be displayed in notebook AND saved to webpage.